{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import time\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import random\n",
    "sns.set()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "seaborn==0.9.0\n",
      "pandas==0.23.4\n",
      "numpy==1.14.5\n",
      "matplotlib==3.0.2\n"
     ]
    }
   ],
   "source": [
    "import pkg_resources\n",
    "import types\n",
    "\n",
    "\n",
    "def get_imports():\n",
    "    for name, val in globals().items():\n",
    "        if isinstance(val, types.ModuleType):\n",
    "            name = val.__name__.split('.')[0]\n",
    "        elif isinstance(val, type):\n",
    "            name = val.__module__.split('.')[0]\n",
    "        poorly_named_packages = {'PIL': 'Pillow', 'sklearn': 'scikit-learn'}\n",
    "        if name in poorly_named_packages.keys():\n",
    "            name = poorly_named_packages[name]\n",
    "        yield name\n",
    "\n",
    "\n",
    "imports = list(set(get_imports()))\n",
    "requirements = []\n",
    "for m in pkg_resources.working_set:\n",
    "    if m.project_name in imports and m.project_name != 'pip':\n",
    "        requirements.append((m.project_name, m.version))\n",
    "\n",
    "for r in requirements:\n",
    "    print('{}=={}'.format(*r))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_state(data, t, n):\n",
    "    d = t - n + 1\n",
    "    block = data[d : t + 1] if d >= 0 else -d * [data[0]] + data[0 : t + 1]\n",
    "    res = []\n",
    "    for i in range(n - 1):\n",
    "        res.append(block[i + 1] - block[i])\n",
    "    return np.array([res])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "TSLA Time Period: **Mar 23, 2018 - Mar 23, 2019**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018-03-23</td>\n",
       "      <td>311.250000</td>\n",
       "      <td>311.250000</td>\n",
       "      <td>300.450012</td>\n",
       "      <td>301.540009</td>\n",
       "      <td>301.540009</td>\n",
       "      <td>6654900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018-03-26</td>\n",
       "      <td>307.339996</td>\n",
       "      <td>307.589996</td>\n",
       "      <td>291.359985</td>\n",
       "      <td>304.179993</td>\n",
       "      <td>304.179993</td>\n",
       "      <td>8375200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-03-27</td>\n",
       "      <td>304.000000</td>\n",
       "      <td>304.269989</td>\n",
       "      <td>277.179993</td>\n",
       "      <td>279.179993</td>\n",
       "      <td>279.179993</td>\n",
       "      <td>13872000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-03-28</td>\n",
       "      <td>264.579987</td>\n",
       "      <td>268.679993</td>\n",
       "      <td>252.100006</td>\n",
       "      <td>257.779999</td>\n",
       "      <td>257.779999</td>\n",
       "      <td>21001400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-03-29</td>\n",
       "      <td>256.489990</td>\n",
       "      <td>270.959991</td>\n",
       "      <td>248.210007</td>\n",
       "      <td>266.130005</td>\n",
       "      <td>266.130005</td>\n",
       "      <td>15170700</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date        Open        High         Low       Close   Adj Close  \\\n",
       "0  2018-03-23  311.250000  311.250000  300.450012  301.540009  301.540009   \n",
       "1  2018-03-26  307.339996  307.589996  291.359985  304.179993  304.179993   \n",
       "2  2018-03-27  304.000000  304.269989  277.179993  279.179993  279.179993   \n",
       "3  2018-03-28  264.579987  268.679993  252.100006  257.779999  257.779999   \n",
       "4  2018-03-29  256.489990  270.959991  248.210007  266.130005  266.130005   \n",
       "\n",
       "     Volume  \n",
       "0   6654900  \n",
       "1   8375200  \n",
       "2  13872000  \n",
       "3  21001400  \n",
       "4  15170700  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('../dataset/TSLA.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "close = df.Close.values.tolist()\n",
    "window_size = 30\n",
    "skip = 1\n",
    "l = len(close) - 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Deep_Evolution_Strategy:\n",
    "\n",
    "    inputs = None\n",
    "\n",
    "    def __init__(\n",
    "        self, weights, reward_function, population_size, sigma, learning_rate\n",
    "    ):\n",
    "        self.weights = weights\n",
    "        self.reward_function = reward_function\n",
    "        self.population_size = population_size\n",
    "        self.sigma = sigma\n",
    "        self.learning_rate = learning_rate\n",
    "\n",
    "    def _get_weight_from_population(self, weights, population):\n",
    "        weights_population = []\n",
    "        for index, i in enumerate(population):\n",
    "            jittered = self.sigma * i\n",
    "            weights_population.append(weights[index] + jittered)\n",
    "        return weights_population\n",
    "\n",
    "    def get_weights(self):\n",
    "        return self.weights\n",
    "\n",
    "    def train(self, epoch = 100, print_every = 1):\n",
    "        lasttime = time.time()\n",
    "        for i in range(epoch):\n",
    "            population = []\n",
    "            rewards = np.zeros(self.population_size)\n",
    "            for k in range(self.population_size):\n",
    "                x = []\n",
    "                for w in self.weights:\n",
    "                    x.append(np.random.randn(*w.shape))\n",
    "                population.append(x)\n",
    "            for k in range(self.population_size):\n",
    "                weights_population = self._get_weight_from_population(\n",
    "                    self.weights, population[k]\n",
    "                )\n",
    "                rewards[k] = self.reward_function(weights_population)\n",
    "            rewards = (rewards - np.mean(rewards)) / np.std(rewards)\n",
    "            for index, w in enumerate(self.weights):\n",
    "                A = np.array([p[index] for p in population])\n",
    "                self.weights[index] = (\n",
    "                    w\n",
    "                    + self.learning_rate\n",
    "                    / (self.population_size * self.sigma)\n",
    "                    * np.dot(A.T, rewards).T\n",
    "                )\n",
    "            if (i + 1) % print_every == 0:\n",
    "                print(\n",
    "                    'iter %d. reward: %f'\n",
    "                    % (i + 1, self.reward_function(self.weights))\n",
    "                )\n",
    "        print('time taken to train:', time.time() - lasttime, 'seconds')\n",
    "\n",
    "\n",
    "class Model:\n",
    "    def __init__(self, input_size, layer_size, output_size):\n",
    "        self.weights = [\n",
    "            np.random.randn(input_size, layer_size),\n",
    "            np.random.randn(layer_size, output_size),\n",
    "            np.random.randn(layer_size, 1),\n",
    "            np.random.randn(1, layer_size),\n",
    "        ]\n",
    "\n",
    "    def predict(self, inputs):\n",
    "        feed = np.dot(inputs, self.weights[0]) + self.weights[-1]\n",
    "        decision = np.dot(feed, self.weights[1])\n",
    "        buy = np.dot(feed, self.weights[2])\n",
    "        return decision, buy\n",
    "\n",
    "    def get_weights(self):\n",
    "        return self.weights\n",
    "\n",
    "    def set_weights(self, weights):\n",
    "        self.weights = weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Agent:\n",
    "\n",
    "    POPULATION_SIZE = 15\n",
    "    SIGMA = 0.1\n",
    "    LEARNING_RATE = 0.03\n",
    "\n",
    "    def __init__(self, model, money, max_buy, max_sell):\n",
    "        self.model = model\n",
    "        self.initial_money = money\n",
    "        self.max_buy = max_buy\n",
    "        self.max_sell = max_sell\n",
    "        self.es = Deep_Evolution_Strategy(\n",
    "            self.model.get_weights(),\n",
    "            self.get_reward,\n",
    "            self.POPULATION_SIZE,\n",
    "            self.SIGMA,\n",
    "            self.LEARNING_RATE,\n",
    "        )\n",
    "\n",
    "    def act(self, sequence):\n",
    "        decision, buy = self.model.predict(np.array(sequence))\n",
    "        return np.argmax(decision[0]), int(buy[0])\n",
    "\n",
    "    def get_reward(self, weights):\n",
    "        initial_money = self.initial_money\n",
    "        starting_money = initial_money\n",
    "        self.model.weights = weights\n",
    "        state = get_state(close, 0, window_size + 1)\n",
    "        inventory = []\n",
    "        quantity = 0\n",
    "        for t in range(0, l, skip):\n",
    "            action, buy = self.act(state)\n",
    "            next_state = get_state(close, t + 1, window_size + 1)\n",
    "            if action == 1 and initial_money >= close[t]:\n",
    "                if buy < 0:\n",
    "                    buy = 1\n",
    "                if buy > self.max_buy:\n",
    "                    buy_units = self.max_buy\n",
    "                else:\n",
    "                    buy_units = buy\n",
    "                total_buy = buy_units * close[t]\n",
    "                initial_money -= total_buy\n",
    "                inventory.append(total_buy)\n",
    "                quantity += buy_units\n",
    "            elif action == 2 and len(inventory) > 0:\n",
    "                if quantity > self.max_sell:\n",
    "                    sell_units = self.max_sell\n",
    "                else:\n",
    "                    sell_units = quantity\n",
    "                quantity -= sell_units\n",
    "                total_sell = sell_units * close[t]\n",
    "                initial_money += total_sell\n",
    "\n",
    "            state = next_state\n",
    "        return ((initial_money - starting_money) / starting_money) * 100\n",
    "\n",
    "    def fit(self, iterations, checkpoint):\n",
    "        self.es.train(iterations, print_every = checkpoint)\n",
    "\n",
    "    def buy(self):\n",
    "        initial_money = self.initial_money\n",
    "        state = get_state(close, 0, window_size + 1)\n",
    "        starting_money = initial_money\n",
    "        states_sell = []\n",
    "        states_buy = []\n",
    "        inventory = []\n",
    "        quantity = 0\n",
    "        for t in range(0, l, skip):\n",
    "            action, buy = self.act(state)\n",
    "            next_state = get_state(close, t + 1, window_size + 1)\n",
    "            if action == 1 and initial_money >= close[t]:\n",
    "                if buy < 0:\n",
    "                    buy = 1\n",
    "                if buy > self.max_buy:\n",
    "                    buy_units = self.max_buy\n",
    "                else:\n",
    "                    buy_units = buy\n",
    "                total_buy = buy_units * close[t]\n",
    "                initial_money -= total_buy\n",
    "                inventory.append(total_buy)\n",
    "                quantity += buy_units\n",
    "                states_buy.append(t)\n",
    "                print(\n",
    "                    'day %d: buy %d units at price %f, total balance %f'\n",
    "                    % (t, buy_units, total_buy, initial_money)\n",
    "                )\n",
    "            elif action == 2 and len(inventory) > 0:\n",
    "                bought_price = inventory.pop(0)\n",
    "                if quantity > self.max_sell:\n",
    "                    sell_units = self.max_sell\n",
    "                else:\n",
    "                    sell_units = quantity\n",
    "                if sell_units < 1:\n",
    "                    continue\n",
    "                quantity -= sell_units\n",
    "                total_sell = sell_units * close[t]\n",
    "                initial_money += total_sell\n",
    "                states_sell.append(t)\n",
    "                try:\n",
    "                    invest = ((total_sell - bought_price) / bought_price) * 100\n",
    "                except:\n",
    "                    invest = 0\n",
    "                print(\n",
    "                    'day %d, sell %d units at price %f, investment %f %%, total balance %f,'\n",
    "                    % (t, sell_units, total_sell, invest, initial_money)\n",
    "                )\n",
    "            state = next_state\n",
    "\n",
    "        invest = ((initial_money - starting_money) / starting_money) * 100\n",
    "        print(\n",
    "            '\\ntotal gained %f, total investment %f %%'\n",
    "            % (initial_money - starting_money, invest)\n",
    "        )\n",
    "        plt.figure(figsize = (20, 10))\n",
    "        plt.plot(close, label = 'true close', c = 'g')\n",
    "        plt.plot(\n",
    "            close, 'X', label = 'predict buy', markevery = states_buy, c = 'b'\n",
    "        )\n",
    "        plt.plot(\n",
    "            close, 'o', label = 'predict sell', markevery = states_sell, c = 'r'\n",
    "        )\n",
    "        plt.legend()\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iter 10. reward: 22.809600\n",
      "iter 20. reward: 51.687003\n",
      "iter 30. reward: 61.576206\n",
      "iter 40. reward: 69.384603\n",
      "iter 50. reward: 74.372100\n",
      "iter 60. reward: 86.872802\n",
      "iter 70. reward: 95.984703\n",
      "iter 80. reward: 86.611603\n",
      "iter 90. reward: 91.603299\n",
      "iter 100. reward: 97.332000\n",
      "iter 110. reward: 97.179203\n",
      "iter 120. reward: 99.749703\n",
      "iter 130. reward: 100.879403\n",
      "iter 140. reward: 87.869305\n",
      "iter 150. reward: 95.844503\n",
      "iter 160. reward: 103.064303\n",
      "iter 170. reward: 108.591401\n",
      "iter 180. reward: 113.703303\n",
      "iter 190. reward: 109.320401\n",
      "iter 200. reward: 114.320704\n",
      "iter 210. reward: 118.800302\n",
      "iter 220. reward: 120.808302\n",
      "iter 230. reward: 116.255301\n",
      "iter 240. reward: 118.316202\n",
      "iter 250. reward: 118.671802\n",
      "iter 260. reward: 118.965402\n",
      "iter 270. reward: 118.079800\n",
      "iter 280. reward: 115.773998\n",
      "iter 290. reward: 109.795800\n",
      "iter 300. reward: 116.520801\n",
      "iter 310. reward: 119.137195\n",
      "iter 320. reward: 118.383199\n",
      "iter 330. reward: 114.609903\n",
      "iter 340. reward: 125.628802\n",
      "iter 350. reward: 121.527300\n",
      "iter 360. reward: 121.432399\n",
      "iter 370. reward: 118.581801\n",
      "iter 380. reward: 119.989300\n",
      "iter 390. reward: 120.004502\n",
      "iter 400. reward: 124.851201\n",
      "iter 410. reward: 122.869297\n",
      "iter 420. reward: 123.599999\n",
      "iter 430. reward: 126.341600\n",
      "iter 440. reward: 127.074699\n",
      "iter 450. reward: 128.540102\n",
      "iter 460. reward: 126.781902\n",
      "iter 470. reward: 128.691898\n",
      "iter 480. reward: 127.336802\n",
      "iter 490. reward: 127.829601\n",
      "iter 500. reward: 129.304401\n",
      "time taken to train: 36.91986346244812 seconds\n"
     ]
    }
   ],
   "source": [
    "model = Model(window_size, 500, 3)\n",
    "agent = Agent(model, 10000, 5, 5)\n",
    "agent.fit(500, 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "day 2: buy 5 units at price 1395.899965, total balance 8604.100035\n",
      "day 3: buy 5 units at price 1288.899995, total balance 7315.200040\n",
      "day 4: buy 5 units at price 1330.650025, total balance 5984.550015\n",
      "day 5: buy 5 units at price 1262.399980, total balance 4722.150035\n",
      "day 6: buy 5 units at price 1337.649995, total balance 3384.500040\n",
      "day 8, sell 5 units at price 1528.600005, investment 9.506415 %, total balance 4913.100045,\n",
      "day 11, sell 5 units at price 1523.500060, investment 18.201572 %, total balance 6436.600105,\n",
      "day 12, sell 5 units at price 1504.649965, investment 13.076311 %, total balance 7941.250070,\n",
      "day 13: buy 5 units at price 1470.399935, total balance 6470.850135\n",
      "day 14, sell 5 units at price 1501.699980, investment 18.955957 %, total balance 7972.550115,\n",
      "day 15: buy 5 units at price 1456.049955, total balance 6516.500160\n",
      "day 16: buy 5 units at price 1438.450010, total balance 5078.050150\n",
      "day 18, sell 5 units at price 1500.399935, investment 12.166855 %, total balance 6578.450085,\n",
      "day 19: buy 5 units at price 1451.199950, total balance 5127.250135\n",
      "day 22: buy 5 units at price 1403.450010, total balance 3723.800125\n",
      "day 24, sell 5 units at price 1470.399935, investment 0.000000 %, total balance 5194.200060,\n",
      "day 25: buy 5 units at price 1469.499970, total balance 3724.700090\n",
      "day 26, sell 5 units at price 1499.600065, investment 2.990976 %, total balance 5224.300155,\n",
      "day 27, sell 5 units at price 1505.749970, investment 4.678644 %, total balance 6730.050125,\n",
      "day 28: buy 1 units at price 284.450012, total balance 6445.600113\n",
      "day 29: buy 1 units at price 294.089996, total balance 6151.510117\n",
      "day 30, sell 5 units at price 1513.849945, investment 4.317117 %, total balance 7665.360062,\n",
      "day 31: buy 5 units at price 1509.850005, total balance 6155.510057\n",
      "day 32, sell 5 units at price 1534.250030, investment 9.319892 %, total balance 7689.760087,\n",
      "day 34, sell 5 units at price 1505.299990, investment 2.436204 %, total balance 9195.060077,\n",
      "day 35: buy 5 units at price 1459.850005, total balance 7735.210072\n",
      "day 36: buy 5 units at price 1420.899965, total balance 6314.310107\n",
      "day 37, sell 5 units at price 1432.400055, investment 403.568288 %, total balance 7746.710162,\n",
      "day 38: buy 5 units at price 1422.700045, total balance 6324.010117\n",
      "day 39: buy 5 units at price 1384.100035, total balance 4939.910082\n",
      "day 41: buy 5 units at price 1375.050050, total balance 3564.860032\n",
      "day 42: buy 5 units at price 1395.350035, total balance 2169.509997\n",
      "day 44: buy 5 units at price 1394.250030, total balance 775.259967\n",
      "day 45: buy 5 units at price 1418.800050, total balance -643.540083\n",
      "day 49, sell 5 units at price 1483.699950, investment 404.505413 %, total balance 840.159867,\n",
      "day 50: buy 5 units at price 1455.650025, total balance -615.490158\n",
      "day 51, sell 5 units at price 1597.500000, investment 5.805212 %, total balance 982.009842,\n",
      "day 53: buy 5 units at price 1588.300020, total balance -606.290178\n",
      "day 54, sell 5 units at price 1660.500030, investment 13.744564 %, total balance 1054.209852,\n",
      "day 55, sell 5 units at price 1713.849945, investment 20.617214 %, total balance 2768.059797,\n",
      "day 56, sell 5 units at price 1723.899995, investment 21.171009 %, total balance 4491.959792,\n",
      "day 57, sell 5 units at price 1788.600005, investment 29.224764 %, total balance 6280.559797,\n",
      "day 58, sell 5 units at price 1790.850065, investment 30.238900 %, total balance 8071.409862,\n",
      "day 59, sell 5 units at price 1854.149935, investment 32.880631 %, total balance 9925.559797,\n",
      "day 61, sell 5 units at price 1811.100005, investment 29.897792 %, total balance 11736.659802,\n",
      "day 62, sell 5 units at price 1737.550050, investment 22.466168 %, total balance 13474.209852,\n",
      "day 63: buy 1 units at price 333.630005, total balance 13140.579847\n",
      "day 64, sell 3 units at price 999.030030, investment -31.368803 %, total balance 14139.609877,\n",
      "day 69: buy 1 units at price 335.070007, total balance 13804.539870\n",
      "day 70: buy 1 units at price 310.859985, total balance 13493.679885\n",
      "day 72: buy 5 units at price 1544.499970, total balance 11949.179915\n",
      "day 73, sell 5 units at price 1592.550050, investment 375.288751 %, total balance 13541.729965,\n",
      "day 75: buy 1 units at price 318.959991, total balance 13222.769974\n",
      "day 76: buy 5 units at price 1583.549955, total balance 11639.220019\n",
      "day 78: buy 5 units at price 1550.500030, total balance 10088.719989\n",
      "day 79: buy 1 units at price 322.690002, total balance 9766.029987\n",
      "day 82: buy 5 units at price 1567.899935, total balance 8198.130052\n",
      "day 83: buy 1 units at price 303.200012, total balance 7894.930040\n",
      "day 84: buy 5 units at price 1487.149965, total balance 6407.780075\n",
      "day 85: buy 1 units at price 308.739990, total balance 6099.040085\n",
      "day 86: buy 5 units at price 1533.249970, total balance 4565.790115\n",
      "day 87: buy 1 units at price 297.179993, total balance 4268.610122\n",
      "day 88: buy 5 units at price 1450.850065, total balance 2817.760057\n",
      "day 89: buy 5 units at price 1490.700075, total balance 1327.059982\n",
      "day 91, sell 5 units at price 1747.700045, investment 462.214543 %, total balance 3074.760027,\n",
      "day 92, sell 5 units at price 1740.850065, investment 12.712858 %, total balance 4815.610092,\n",
      "day 93: buy 1 units at price 341.989990, total balance 4473.620102\n",
      "day 94, sell 5 units at price 1897.850035, investment 495.011941 %, total balance 6371.470137,\n",
      "day 95, sell 5 units at price 1851.699980, investment 16.933474 %, total balance 8223.170117,\n",
      "day 96: buy 1 units at price 352.450012, total balance 7870.720105\n",
      "day 97, sell 5 units at price 1777.449950, investment 14.637208 %, total balance 9648.170055,\n",
      "day 98, sell 5 units at price 1782.050020, investment 452.248291 %, total balance 11430.220075,\n",
      "day 99, sell 5 units at price 1738.200075, investment 10.861671 %, total balance 13168.420150,\n",
      "day 101, sell 5 units at price 1677.250060, investment 453.182716 %, total balance 14845.670210,\n",
      "day 102: buy 5 units at price 1527.500000, total balance 13318.170210\n",
      "day 103: buy 1 units at price 308.440002, total balance 13009.730208\n",
      "day 104, sell 5 units at price 1609.499970, investment 8.227146 %, total balance 14619.230178,\n",
      "day 105, sell 5 units at price 1608.200075, investment 420.891406 %, total balance 16227.430253,\n",
      "day 118: buy 5 units at price 1397.200010, total balance 14830.230243\n",
      "day 119: buy 5 units at price 1452.700045, total balance 13377.530198\n",
      "day 121: buy 5 units at price 1476.000060, total balance 11901.530138\n",
      "day 122: buy 5 units at price 1474.199980, total balance 10427.330158\n",
      "day 124, sell 5 units at price 1495.099945, investment 7.006866 %, total balance 11922.430103,\n",
      "day 125, sell 5 units at price 1491.649935, investment 2.681207 %, total balance 13414.080038,\n",
      "day 126: buy 1 units at price 299.100006, total balance 13114.980032\n",
      "day 127, sell 5 units at price 1498.399965, investment 1.517609 %, total balance 14613.379997,\n",
      "day 128: buy 5 units at price 1504.949950, total balance 13108.430047\n",
      "day 129, sell 5 units at price 1547.899935, investment 4.999319 %, total balance 14656.329982,\n",
      "day 131: buy 5 units at price 1323.849945, total balance 13332.480037\n",
      "day 132, sell 5 units at price 1553.500060, investment 419.391517 %, total balance 14885.980097,\n",
      "day 135: buy 1 units at price 281.829987, total balance 14604.150110\n",
      "day 136: buy 5 units at price 1309.750060, total balance 13294.400050\n",
      "day 137: buy 5 units at price 1252.799990, total balance 12041.600060\n",
      "day 139: buy 5 units at price 1284.400025, total balance 10757.200035\n",
      "day 140: buy 5 units at price 1261.149980, total balance 9496.050055\n",
      "day 142: buy 5 units at price 1297.949980, total balance 8198.100075\n",
      "day 143: buy 1 units at price 276.589996, total balance 7921.510079\n",
      "day 145: buy 5 units at price 1319.550020, total balance 6601.960059\n",
      "day 146: buy 1 units at price 260.000000, total balance 6341.960059\n",
      "day 147: buy 5 units at price 1304.750060, total balance 5037.209999\n",
      "day 148, sell 5 units at price 1470.700075, investment -2.275815 %, total balance 6507.910074,\n",
      "day 150: buy 5 units at price 1574.299925, total balance 4933.610149\n",
      "day 151, sell 5 units at price 1654.499970, investment 24.976398 %, total balance 6588.110119,\n",
      "day 153: buy 5 units at price 1649.499970, total balance 4938.610149\n",
      "day 155, sell 5 units at price 1721.399995, investment 510.793767 %, total balance 6660.010144,\n",
      "day 156: buy 1 units at price 346.410004, total balance 6313.600140\n",
      "day 157, sell 5 units at price 1706.999970, investment 30.330207 %, total balance 8020.600110,\n",
      "day 158, sell 5 units at price 1705.299990, investment 36.119094 %, total balance 9725.900100,\n",
      "day 159: buy 1 units at price 348.160004, total balance 9377.740096\n",
      "day 160, sell 5 units at price 1756.999970, investment 36.795386 %, total balance 11134.740066,\n",
      "day 162: buy 1 units at price 331.279999, total balance 10803.460067\n",
      "day 164, sell 5 units at price 1720.000000, investment 36.383462 %, total balance 12523.460067,\n",
      "day 166, sell 5 units at price 1771.549990, investment 36.488310 %, total balance 14295.010057,\n",
      "day 167, sell 5 units at price 1767.350005, investment 538.978282 %, total balance 16062.360062,\n",
      "day 168: buy 1 units at price 347.489990, total balance 15714.870072\n",
      "day 169: buy 1 units at price 338.190002, total balance 15376.680070\n",
      "day 170: buy 1 units at price 325.829987, total balance 15050.850083\n",
      "day 171, sell 5 units at price 1730.000000, investment 31.105299 %, total balance 16780.850083,\n",
      "day 173, sell 5 units at price 1739.349975, investment 568.980760 %, total balance 18520.200058,\n",
      "day 175: buy 5 units at price 1752.400055, total balance 16767.800003\n",
      "day 178, sell 5 units at price 1815.299990, investment 39.130094 %, total balance 18583.099993,\n",
      "day 179, sell 5 units at price 1789.850005, investment 13.691805 %, total balance 20372.949998,\n",
      "day 189: buy 1 units at price 319.769989, total balance 20053.180009\n",
      "day 190: buy 5 units at price 1476.950075, total balance 18576.229934\n",
      "day 191, sell 5 units at price 1630.449980, investment 409.882114 %, total balance 20206.679914,\n",
      "day 195: buy 1 units at price 310.119995, total balance 19896.559919\n",
      "day 196: buy 5 units at price 1501.799925, total balance 18394.759994\n",
      "day 197, sell 5 units at price 1588.450010, investment 7.549337 %, total balance 19983.210004,\n",
      "day 198, sell 2 units at price 669.919982, investment 116.019603 %, total balance 20653.129986,\n",
      "day 202: buy 1 units at price 347.260010, total balance 20305.869976\n",
      "day 205, sell 1 units at price 346.049988, investment -0.348448 %, total balance 20651.919964,\n",
      "day 207: buy 1 units at price 302.260010, total balance 20349.659954\n",
      "day 208, sell 1 units at price 298.920013, investment -1.105008 %, total balance 20648.579967,\n",
      "day 209: buy 5 units at price 1437.949980, total balance 19210.629987\n",
      "day 210: buy 5 units at price 1457.550050, total balance 17753.079937\n",
      "day 212: buy 5 units at price 1481.900025, total balance 16271.179912\n",
      "day 213: buy 1 units at price 297.459991, total balance 15973.719921\n",
      "day 215: buy 1 units at price 307.019989, total balance 15666.699932\n",
      "day 216, sell 5 units at price 1561.049955, investment 8.560797 %, total balance 17227.749887,\n",
      "day 217, sell 5 units at price 1564.450075, investment 7.334227 %, total balance 18792.199962,\n",
      "day 218, sell 5 units at price 1606.750030, investment 8.424995 %, total balance 20398.949992,\n",
      "day 219, sell 2 units at price 634.440002, investment 113.285827 %, total balance 21033.389994,\n",
      "day 220: buy 1 units at price 307.510010, total balance 20725.879984\n",
      "day 221: buy 5 units at price 1528.999940, total balance 19196.880044\n",
      "day 222: buy 1 units at price 312.839996, total balance 18884.040048\n",
      "day 224: buy 1 units at price 308.170013, total balance 18575.870035\n",
      "day 225, sell 5 units at price 1518.849945, investment 394.707185 %, total balance 20094.719980,\n",
      "day 229: buy 5 units at price 1456.150055, total balance 18638.569925\n",
      "day 230, sell 5 units at price 1473.549955, investment 379.187638 %, total balance 20112.119880,\n",
      "day 232: buy 1 units at price 297.859985, total balance 19814.259895\n",
      "day 233, sell 4 units at price 1258.959960, investment -17.661216 %, total balance 21073.219855,\n",
      "day 245: buy 5 units at price 1377.149965, total balance 19696.069890\n",
      "day 246: buy 1 units at price 269.489990, total balance 19426.579900\n",
      "day 247, sell 5 units at price 1337.350005, investment -2.890024 %, total balance 20763.929905,\n",
      "day 248, sell 1 units at price 273.600006, investment 1.525109 %, total balance 21037.529911,\n",
      "\n",
      "total gained 11037.529911, total investment 110.375299 %\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "agent.buy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
